Performance Combustion and Emission Characteristics of 3-Cylinder SI Engine Fuelled Conventional Gasoline, Ethanol Blends, and Micro-emulsion Used as an Alternative Fuel
Bibliographic record
Abstract
In this contribution 15% ethanol blend with 85% gasoline, and micro-emulsion fuel (90% gasoline, 8% ethanol, & 2% H2O) was compared with 100% conventional gasoline fuel on 3-cylinder Spark Ignition engine to check its performance and emission characteristics. The AVL Boost software was used for predicting the performance and emission characteristics of 3-cylinder SI engine. It was found that although power increases for conventional 100% gasoline as the fuel, as compared to 15% ethanol blends and micro-emulsion fuel, but emissions were reduced more for micro-emulsion used as an alternative fuel in SI engine, and it meets the standards of present emission norms Euro 6. Micro-emulsion fuel was prepared in the lab by blending gasoline, ethanol and H2O was used as an additive in the fuel for improving its physical & thermal properties. The H2O molecule present in the fuel helped in reducing the emissions from 3-cylinder SI engine. As H2O addition increases the oxygen concentration in the micro-emulsion fuel which helps in enhancing the combustion of fuel, and in build oxygen atom inside the ethanol fuel also helps in better combustion which ultimately gives less CO, HC, & NOx emissions. The vibe-2 zone model was used for the combustion analysis of the engine which involves both burnt and unburnt gases in the combustion chamber. The results showed that micro-emulsion fuel not only improves the performance but also reduces the emissions. All the emissions CO, HC, and NOx showed decrease for ethanol blended fuel and micro-emulsion as compared to 100% gasoline fuel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".